{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fall / Non Fall Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Rows, Columns) in training dataset (480, 136)\n",
      "Number of Features in training dataset:- 136\n",
      "Number of Features in testing dataset:- 136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn import ensemble\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_selection import VarianceThreshold, RFE\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Random values generated should be same everytime the program is run\n",
    "np.random.seed(7)\n",
    "\n",
    "#Reading of Training and Testing Data from CSV File\n",
    "data=pd.read_csv(\"Fall20.csv\")\n",
    "train=data.iloc[0:480,:]\n",
    "test=data.iloc[480:,:]\n",
    "print(\"(Rows, Columns) in training dataset\",train.shape)\n",
    "print(\"Number of Features in training dataset:-\",train.shape[1])\n",
    "print(\"Number of Features in testing dataset:-\",test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "Replace NaN values with minimun of that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "#Transforming Columns containg \"NaN\" Values\n",
    "#in both training and testing dataset\n",
    "for col in train.columns:\n",
    "    val = train[col].min()\n",
    "    train[col]=train[col].fillna(value=val,axis=0)\n",
    "\n",
    "for col in test.columns:\n",
    "    val = test[col].min()\n",
    "    test[col]=test[col].fillna(value=val,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Train and Test Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Separating training data Features and Predictions into different dataframes\n",
    "X_train=train.drop('class_label',axis=1)\n",
    "Y_train=train['class_label']\n",
    "\n",
    "#Separating testing data Features and Predictions into different dataframes\n",
    "X_test=test.drop('class_label',axis=1)\n",
    "Y_test=test['class_label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c7f94a5f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sQZ9kDfB3wKXAucBVSc4dx3tJkhY2riP684Dpqnq8qv4XuA3YMqb3kiQtYMk3Bx9iHbBv\n3voM8K75A5JsA7a11eeSPDqmXk5EZwI/X+0mhstqN6Bj7/j42cxx87P5u6MMGlfQD/qvVL+xUrUd\n2D6m9z+hJZmqqsnV7kM6kj+bq2NcUzczwIZ56+uB/WN6L0nSAsYV9N8FNiU5J8kpwJXAzjG9lyRp\nAWOZuqmqF5JcB/wrsAa4tar2jOO9NJBTYnql8mdzFaSqho+SJB23vDJWkjpn0EtS5wx6SercuM6j\n1zGU5C3MXXm8jrnrFfYDO6tq76o2JukVwSP641ySjzH3FRMBHmLu1NYAX/XL5CSBZ90c95L8J/C2\nqvq/I+qnAHuqatPqdCYdXZKrq+qLq93HicIj+uPfi8AbB9TXtm3SK9GnVruBE4lz9Me/DwP3JHmM\nl75I7neANwPXrVpXOuEl2X20TcDZx7KXE51TNx1I8irmvhp6HXP/E80A362qQ6vamE5oSZ4E3gM8\nfeQm4D+qatC/RDUGHtF3oKpeBB5Y7T6kI9wJvLaqdh25Icn9x76dE5dH9JLUOf8YK0mdM+glqXMG\nvSR1zqCXpM79P/hY7DvHtOZyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c7f985e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.class_label.value_counts().plot(kind='bar',colors='YR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "### Remove Low Variance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features in training dataset(before feature selection by Variance Threshold):- 135\n",
      "Number of Features in training dataset(after feature selection by Variance Threshold):- 129\n"
     ]
    }
   ],
   "source": [
    "#Performing Feature Selection on Training Dataset\n",
    "print(\"Number of Features in training dataset(before feature selection by Variance Threshold):-\",X_train.shape[1])\n",
    "X_train_temp = X_train.copy(deep=True)  # Make a deep copy of the Training Data dataframe\n",
    "selector = VarianceThreshold(0.12)\n",
    "selector.fit(X_train_temp)\n",
    "X_res = X_train_temp.loc[:, selector.get_support(indices=False)]\n",
    "X_train=X_res\n",
    "print(\"Number of Features in training dataset(after feature selection by Variance Threshold):-\",X_train.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between each feature and class variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find most important features relative to target through correlation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/inderpreet/anaconda3/lib/python3.6/site-packages/pandas/plotting/_core.py:179: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
      "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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4Ih3XkZvZQBNESYeb2WGSTujxsVm+t+z1ePz0nzdp68L64TJ8kHNMpS7j7PBr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\nCihm8leq82yTXaqIMQHljBM6awXzmgqwcpnEOrT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YKMg2VMVhm3DQ/r6vX4avVY/gW2Mz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c7f985d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "traini = pd.concat([X_train, Y_train], axis=1, join='inner')\n",
    "\n",
    "# Find most important features relative to target i.e finding correlation of every individual feature i.e independent variable with dependent variable and then sorting them and using the features that have maximum correlation\n",
    "print(\"Find most important features relative to target through correlation\")\n",
    "corr = traini.corr()\n",
    "corr.sort_values([\"class_label\"], ascending = False, inplace = True)\n",
    "\n",
    "#Selecting top-20 features\n",
    "corr.class_label[1:20].plot(kind='bar',colors='RGYBC')\n",
    "top_features=corr.class_label[1:20].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top 20 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mean_waistAccelerationY', 'mean_waistAngularVelocityZ', 'var_waistAccelerationX', 'var_headAccelerationY', 'var_sternumAccelerationY', 'var_waistAccelerationY', 'mean_headAccelerationY', 'var_waistAngularVelocityZ', 'var_rthighMagneticFieldX', 'var_sternumAccelerationX', 'mean_sternumAccelerationY', 'var_rankleMagneticFieldX', 'var_rthighAccelerationZ', 'mean_rthighAccelerationZ', 'var_sternumAngularVelocityZ', 'var_waistMagneticFieldX', 'mean_waistAngularVelocityX', 'var_sternumMagneticFieldX', 'var_rankleMagneticFieldZ']\n"
     ]
    }
   ],
   "source": [
    "features=[]\n",
    "columns=top_features.index\n",
    "for col in columns:\n",
    "    features.append(col)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select same features in Training and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "Index(['mean_waistAccelerationY', 'mean_waistAngularVelocityZ',\n",
      "       'var_waistAccelerationX', 'var_headAccelerationY',\n",
      "       'var_sternumAccelerationY', 'var_waistAccelerationY',\n",
      "       'mean_headAccelerationY', 'var_waistAngularVelocityZ',\n",
      "       'var_rthighMagneticFieldX', 'var_sternumAccelerationX',\n",
      "       'mean_sternumAccelerationY', 'var_rankleMagneticFieldX',\n",
      "       'var_rthighAccelerationZ', 'mean_rthighAccelerationZ',\n",
      "       'var_sternumAngularVelocityZ', 'var_waistMagneticFieldX',\n",
      "       'mean_waistAngularVelocityX', 'var_sternumMagneticFieldX',\n",
      "       'var_rankleMagneticFieldZ'],\n",
      "      dtype='object')\n",
      "19\n",
      "Index(['mean_waistAccelerationY', 'mean_waistAngularVelocityZ',\n",
      "       'var_waistAccelerationX', 'var_headAccelerationY',\n",
      "       'var_sternumAccelerationY', 'var_waistAccelerationY',\n",
      "       'mean_headAccelerationY', 'var_waistAngularVelocityZ',\n",
      "       'var_rthighMagneticFieldX', 'var_sternumAccelerationX',\n",
      "       'mean_sternumAccelerationY', 'var_rankleMagneticFieldX',\n",
      "       'var_rthighAccelerationZ', 'mean_rthighAccelerationZ',\n",
      "       'var_sternumAngularVelocityZ', 'var_waistMagneticFieldX',\n",
      "       'mean_waistAngularVelocityX', 'var_sternumMagneticFieldX',\n",
      "       'var_rankleMagneticFieldZ'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Selecting Features for training dataset\n",
    "X_train=X_train[features]\n",
    "print(X_train.shape[1])\n",
    "print(X_train.columns)\n",
    "\n",
    "#Selecting Same Features for testing dataset\n",
    "X_test=X_test[X_train.columns]\n",
    "print(X_test.shape[1])\n",
    "print(X_test.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Models and Comparing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating training and cross-validation dataset\n",
    "X_train,X_CV,Y_train,Y_CV=train_test_split(X_train,Y_train,test_size=0.30)\n",
    "\n",
    "\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    #plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "results_sensitivity={}\n",
    "results_specitivity={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 93.75 Specificity 98.2142857143\n",
      "Error Rate:\n",
      "False Positive Rate- 1.78571428571 True Positive Rate- 6.25\n",
      "Confusion matrix, without normalization\n",
      "[[110   2]\n",
      " [  2  30]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.98214286  0.01785714]\n",
      " [ 0.0625      0.9375    ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c55bb5470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vM2BUwrLmwCHAYOD+eB9GAevcvVe8/nPMrEUS25FS0BXdkqzqZjY7fv8u8AjR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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c55b1f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Applying Logistic Regression\n",
    "log_reg=LogisticRegression()\n",
    "log_reg=log_reg.fit(X_train, Y_train)\n",
    "log_pred=log_reg.predict(X_CV)\n",
    "score=accuracy_score(Y_CV,log_pred)\n",
    "#print(score)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, log_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Logistic']=sensitivity\n",
    "results_specitivity['Logistic']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,log_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 87.5 Specificity 97.3214285714\n",
      "Error Rate:\n",
      "False Positive Rate- 2.67857142857 True Positive Rate- 12.5\n",
      "Confusion matrix, without normalization\n",
      "[[109   3]\n",
      " [  4  28]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.97321429  0.02678571]\n",
      " [ 0.125       0.875     ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c55b219b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JJ5TKImL6avMqClGMmVk+l5l8KGkPICTVA84GphS2LDMrVfn0lM4CzgPaAh8De6bzzMw2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\nh5KZZYpDycwy5f8BdBPPXrgn+zoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c55b216a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Decision Tree\n",
    "decision_reg = DecisionTreeClassifier(criterion='gini',\n",
    "                                            max_depth=10, max_leaf_nodes=23, splitter='best')\n",
    "decision_reg=decision_reg.fit(X_train, Y_train)\n",
    "decision_pred=decision_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, decision_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['DT']=sensitivity\n",
    "results_specitivity['DT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,decision_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 100.0 Specificity 92.8571428571\n",
      "Error Rate:\n",
      "False Positive Rate- 7.14285714286 True Positive Rate- 0.0\n",
      "Confusion matrix, without normalization\n",
      "[[104   8]\n",
      " [  0  32]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.92857143  0.07142857]\n",
      " [ 0.          1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e914fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c5949f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gaussian Naive Bayes\n",
    "naivebay_reg = GaussianNB()\n",
    "naivebay_reg=naivebay_reg.fit(X_train, Y_train)\n",
    "naivebay_pred=naivebay_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, naivebay_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['NB']=sensitivity\n",
    "results_specitivity['NB']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,naivebay_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 0.0 Specificity 100.0\n",
      "Error Rate:\n",
      "False Positive Rate- 0.0 True Positive Rate- 100.0\n",
      "Confusion matrix, without normalization\n",
      "[[112   0]\n",
      " [ 32   0]]\n",
      "Normalized confusion matrix\n",
      "[[ 1.  0.]\n",
      " [ 1.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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5cz/joT/fVnSptgT19fWcctIPuPf+h3jhxVe4a8TtvPrKK0WXVQiHUhshiU6rdgZgwYL5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\n4lAys6Q4lMwsKQ4lM0uKQ8nMkvL/AfIMbSXiErj+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e84e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hwCOSFgKTgH02sohLgbslnUd2l82LIuJpSU/mh9z/no8r7Q08nffUPgTOjogX\nJI0EXgLmkO1ibs7/AZ7J209l3fB7HfgH2f2rLoyIjyX9hmys6YX85noLgFPr9tOxuvK1b2aWFO++\nmVlSHEpmlhSHkpklxaFkZklxKJlZUhxKZpYUh5KZJcWhZGZJ+f83EoQDBY9NJAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c55b3bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Support Vector Machines\n",
    "svm_reg = SVC()\n",
    "svm_reg=svm_reg.fit(X_train, Y_train)\n",
    "svm_pred=svm_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, svm_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "cm=confusion_matrix(Y_CV,svm_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.875 Specificity 99.1071428571\n",
      "Error Rate\n",
      "False Positive Rate- 0.892857142857 True Positive Rate- 3.125\n",
      "Confusion matrix, without normalization\n",
      "[[111   1]\n",
      " [  1  31]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.99107143  0.00892857]\n",
      " [ 0.03125     0.96875   ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c55b3b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3nwdgZk8AZ29mG4cApwG4exGwOr7HL1G/+PVhPF2PKKR2BF509zXxNl5OYp86\nm9mNRIeI9YBxCcueja+Yn2dmX8T70A/YK2G8KTfe9twktiVJUihJsn50966JM+Lg+SFxFvCGu/+6\nVLuuQEVdpWvAcHd/oNQ2LtqGbTwKDHD3j8xsCNA3YVnpdXm87fPdPTG8MLOW5dyulEGHb1KRJgH7\nm1kbADOrY2ZtgTnA7mbWOm736y38/FtEXy9ePH6zE/AdUS+o2DhgaMJYVV78JQr/BY4zs9pmtiPR\noeLW7AgsNbMawKBSywaaWbW45lbAp/G2z43bY2ZtzaxuEtuRclBPSSqMu6+IexxPmdkO8exr3X2u\nmZ0NjDWzlcAEoPNmVnEh8KCZnUn0lM1z3X2imb0bn3J/NR5X6gBMjHtq3wOnuvs0M3sGmA4sJDrE\n3Jo/Au/H7WdSMvw+Bd4men7VOe6+1sz+QTTWNC1+uN4KYEBy/3UkWbr3TUSCosM3EQmKQklEgqJQ\nEpGgKJREJCgKJREJikJJRIKiUBKRoCiURCQo/w8bVbwwJ6zVMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e8f8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying AdaBoost Classifier\n",
    "ada_reg = AdaBoostClassifier(random_state=2, n_estimators=500)\n",
    "ada_reg=ada_reg.fit(X_train, Y_train)\n",
    "ada_pred=ada_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, ada_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['Adaboost']=sensitivity\n",
    "results_specitivity['Adaboost']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,ada_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 93.75 Specificity 99.1071428571\n",
      "Error Rate:\n",
      "False Positive Rate- 0.892857142857 True Positive Rate- 6.25\n",
      "Confusion matrix, without normalization\n",
      "[[111   1]\n",
      " [  2  30]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.99107143  0.00892857]\n",
      " [ 0.0625      0.9375    ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e791cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b+wPfAoiIcmB5eo9frhHp64V0uiVJSG0B3BMRn6TbuC+Pfeoj6Wckh4gtgSk5\ny+5Mr5h/XdKb6T6MAPrmjDe1Src9J49tWZ4cSpavTyOiX+6MNHg+zp0FPBwR36jSrh+wqa7SFTAh\nIq6vso0zNmIbNwOjImKmpHHAsJxlVdcV6bZPjYjc8EJS1xpu16rhwzfblJ4G9pHUDUDS5pK6A7OB\nHSTtlLb7xnp+/lGSrxevGL/ZEviQpBdUYQowPmesqlP6JQr/Bo6Q1FzSFiSHihuyBbBYUhNgTJVl\noyU1SmveEXgt3fYpaXskdZfUIo/tWA24p2SbTEQsSXsct0naLJ19QUTMkXQiMFnSUuBJoM86VnE6\ncIOk40mesnlKRDwlaWp6yv3BdFypF/BU2lP7CPhmREyXdAcwA1hAcoi5IT8Cnknbv0jl8HsNeJzk\n+VUnR8QKSb8nGWuanj5cbwkwKr//OpYv3/tmZpniwzczyxSHkpllikPJzDLFoWRmmeJQMrNMcSiZ\nWaY4lMwsUxxKZpYp/w/dQqz0UgcDyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e75fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Gradient Boosting Classifier\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01}\n",
    "gb_reg = ensemble.GradientBoostingClassifier(**params)\n",
    "gb_reg=gb_reg.fit(X_train, Y_train)\n",
    "gb_pred=gb_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, gb_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['GBT']=sensitivity\n",
    "results_specitivity['GBT']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,gb_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 96.875 Specificity 98.2142857143\n",
      "Error Rate:\n",
      "False Positive Rate- 1.78571428571 True Positive Rate- 3.125\n",
      "Confusion matrix, without normalization\n",
      "[[110   2]\n",
      " [  1  31]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.98214286  0.01785714]\n",
      " [ 0.03125     0.96875   ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c7c01b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uvOTuPeLtzQeGJyxrCRwHDAAejPdhOLDJ3XvE67/YzA5PYjtSCrqiW5JVw8xm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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e773080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Applying Random Forest Classifier\n",
    "rf_reg = RandomForestClassifier(random_state=1)\n",
    "rf_reg=rf_reg.fit(X_train, Y_train)\n",
    "rf_pred=rf_reg.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, rf_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['RForest']=sensitivity\n",
    "results_specitivity['RForest']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,rf_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Majority Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity- 100.0 Specificity 99.1071428571\n",
      "Error Rate:\n",
      "False Positive Rate- 0.892857142857 True Positive Rate- 0.0\n",
      "Confusion matrix, without normalization\n",
      "[[111   1]\n",
      " [  0  32]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.99107143  0.00892857]\n",
      " [ 0.          1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e6b42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NgHeBXfJ5ZmZ1rphr394DDmuAWszMirrz5A2s5hq4iDiuXioys7JWzJjSIwXPWwNfBd6u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\nmQPy04WFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4e65fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying Majority Voting Concept\n",
    "majority_class = VotingClassifier(estimators=[('lr', ada_reg),\n",
    "                                    ('gnb', gb_reg),('naive_bayes',naivebay_reg)],\n",
    "                                    voting='hard')\n",
    "majority_class = majority_class.fit(X_train, Y_train)\n",
    "majority_pred=majority_class.predict(X_CV)\n",
    "\n",
    "tn, fp, fn, tp  = confusion_matrix(Y_CV, majority_pred, labels=[0, 1]).ravel()\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity=tn/(tn+fp)\n",
    "print(\"Sensitivity-\",sensitivity*100,\"Specificity\",specificity*100)\n",
    "print(\"Error Rate:\")\n",
    "fpr=fp/(fp+tn)\n",
    "tpr=fn/(fn+tp)\n",
    "print(\"False Positive Rate-\",fpr*100,\"True Positive Rate-\",tpr*100)\n",
    "\n",
    "results_sensitivity['MVoting']=sensitivity\n",
    "results_specitivity['MVoting']=specificity\n",
    "\n",
    "cm=confusion_matrix(Y_CV,majority_pred,labels=[0,1])\n",
    "classes=[\"NonFall\",\"Fall\"]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm,classes,normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AawaWXcOjBxEbp5ePyqokPwWcBVzS1/KcDOxJJrkzybuTfKXPnef17SuT/FXf/idJvjHL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\nSWqQ4S5JDTLcJalBhrskNchwl6QG/X+M6hwuwvkVzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4d8b26d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_sensitivity)), list(results_sensitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_sensitivity)), list(results_sensitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c4d8c50f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(results_specitivity)), list(results_specitivity.values()),color='RGBYC',width=0.3)\n",
    "plt.xticks(range(len(results_specitivity)), list(results_specitivity.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
